A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: From classical data mining to deep learning

Xin Liu, Zijun Zhang and Zhe Song

Renewable and Sustainable Energy Reviews, 2020, vol. 119, issue C

Abstract: This paper aims at studying the data-driven short-term provincial load forecasting (STLF) problem via an in-depth exploration of benefits brought by the feature engineering and model selection. Three core issues regarding model selections, feature selections, and feature encoding mechanism selections are deeply investigated. The candidate models are grouped into three types: the time series model, classical regression models, and the deep learning models. Three categories of features, historical loads, calendar effects, and weather factors, are considered and utilized in various encoding mechanisms. In experimental studies, an hourly provincial load dataset from Jiangsu Province in China and the corresponding weather records are utilized. The experiments are extensively performed in three parts according to model types. A time series model is conducted individually and the greedy forward wrapper-based feature selections (GFW-FS) are separately performed in six classical regression models to determine suitable encoded features. Deep learning approaches for developing STLF models are also considered. A deep neural network (DNN) model considering selected features of shallow neural networks (SNN) is developed. Meanwhile, a novel convolutional neural network (CNN) based model using GFW-FS is constructed. Through a comparative error analysis of the test set, the intrinsic linear nature among extracted features and the target in the 24-h-ahead provincial STLF problem is discovered. Feature effects are also evaluated. Data-driven models and their considered features, which are more effective to the STLF problem, are reported.

Keywords: Short-term load prediction; Data-driven model; Feature selection; Deep learning; Feature encoding (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.rser.2019.109632

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